# Non-configurable setting to indicate open source build.
bool_setting(
    name = "oss_setting",
    build_setting_default = if_oss(True, False),
    visibility = ["//visibility:private"],
)

# Setting to use when loading kernels dynamically
config_setting(
    name = "dynamic_loaded_kernels",
    define_values = {
        "dynamic_loaded_kernels": "true",
        "framework_shared_object": "true",
    },

      root_dir_ = tensorflow::strings::StrCat(
          "/", tmp_dir_,
          tensorflow::strings::StrCat("tf_fs_", rng_val_, "_", test_name), "/");
    } else {
      root_dir_ = tensorflow::io::JoinPath(
          tmp_dir_,
          tensorflow::strings::StrCat("tf_fs_", rng_val_, "_", test_name));
    }
    if (!GetParam().empty()) {
      root_dir_ = tensorflow::strings::StrCat(GetParam(), "://", cloud_path_,

  // Create a session for this graph.
  CSession csession(graph, s, use_XLA);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  if (!device.empty()) {
    LOG(INFO) << "Setting op Min on device " << device;
  }
  TF_Operation* min = MinWithDevice(feed, one, graph, device, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Run the graph.

#include <string>

// clang-format off
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/platform.h"
// clang-format on

#include "absl/strings/match.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/eager/c_api_internal.h"
#include "tensorflow/c/eager/c_api_test_util.h"
#include "tensorflow/c/eager/tfe_op_internal.h"

# TODO: remove those flags when they are flipped in the default Bazel version TF uses.
build --incompatible_enforce_config_setting_visibility
# TODO: also enable this flag after fixing the visibility violations
# build --incompatible_config_setting_private_default_visibility

# Default options should come above this line.

# Android configs. Bazel needs to have --cpu and --fat_apk_cpu both set to the

  var = environ_cp.get(var_name)
  if var is not None:
    var_content = var.strip().lower()
    true_strings = ('1', 't', 'true', 'y', 'yes')
    false_strings = ('0', 'f', 'false', 'n', 'no')
    if var_content in true_strings:
      var = True
    elif var_content in false_strings:
      var = False
    else:
      raise UserInputError(

/// TensorFlow, `TF_InitPlugin` has a `TF_FilesystemPluginInfo` argument which
/// the plugin must fill (using the `TF_SetFilesystemVersionMetadata` for the
/// metadata and setting up all the supported operations and the URI schemes
/// that are supported).

/// This structure incorporates the operations defined in Section 2 and the

        const string& in = node_def.input(i);
        const auto& v =
            a_edges.insert({in, strings::StrCat(node_def.name(), ":", i)});
        ASSERT_TRUE(v.second) << "Duplicate edge " << in << " -> "
                              << strings::StrCat(node_def.name(), ":", i)
                              << ". fdef: " << fdef.DebugString();
      }
    }

// corresponding entry in the `dims` array must be -1.
//
// This does not overwrite the existing shape associated with `output`,
// but merges the input shape with the existing shape.  For example,
// setting a shape of [-1, 2] with an existing shape [2, -1] would set
// a final shape of [2, 2] based on shape merging semantics.
//
// Returns an error into `status` if:
//   * `output` is not in `graph`.